Toward Near-Real-Time Training With Semi-Random Deep Neural Networks and Tensor-Train Decomposition

Toward Near-Real-Time Training With Semi-Random Deep Neural Networks and Tensor-Train Decomposition

In recent years, deep neural networks have shown to achieve state-of-the-art performance on several classification and prediction tasks. However, these networks demand undesirable lengthy training times coupled with high computational resources (memory, I/O, processing time). In this work, we explor...

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Bibliographic Details
Main Authors: Humza Syed, Ryan Bryla, Uttam Majumder, Dhireesha Kudithipudi
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural networks (CNNs)
extreme learning machine (ELM)
image classification
neural networks with random weights
random vector functional link (RVFL)
synthetic aperture radar (SAR)
Online Access:https://ieeexplore.ieee.org/document/9492908/

Internet

https://ieeexplore.ieee.org/document/9492908/

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